Speech recognition plays an important role in communication systems, for both gathering and supplying information to users. Traditionally, interactive voice response (IVR) systems have relied upon a combination of dual-tone multi-frequency (DTMF) and speech inputs to acquire and process information. However, for complicated transactions requiring a quantity of numbers, letters, and words to be input, the concept of an IVR system has been more appealing than its conception. Namely, typical DTMF interfaces have proven to be impractically slow for complex data entry. As such, organizations are becoming ever reliant upon voice based systems to augment DTMF inputs. Unfortunately, voice based systems have introduced new, more challenging issues pertaining to the intricacies of spoken language and the infinite variations on human utterance. Accordingly, IVR systems implementing speech recognition technology have proven to be unacceptably inaccurate at converting a spoken utterance to a corresponding textual string or other equivalent symbolic representation.
Therefore, there is a need for an improved approach for providing speech recognition.